---
title: "Introduction to genetic.algo.optimizeR"
output: rmarkdown::html_vignette
toc: true
vignette: >
  %\VignetteIndexEntry{Introduction to genetic.algo.optimizeR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
```


## Overview

This vignette demonstrates how to use the `genetic.algo.optimizeR` package to optimize the function \( f(x) = x^2 - 4x + 4 \) using a genetic algorithm.

## Aim

Optimize the function \( f(x) = x^2 - 4x + 4 \) to find the value of \( x \) that minimizes the function.

## Method

### Initial Population

We start with a population of three individuals: \( x_1 = 1 \), \( x_2 = 3 \), and \( x_3 = 0 \).

```{r setup}
# devtools::install_github("danymukesha/genetic.algo.optimizeR", upgrade = c("never"),)
library(genetic.algo.optimizeR)
```


```{r}
# Initialize population
population <- initialize_population(population_size = 3, min = 0, max = 3)
population
```

### Evaluation

We evaluate the fitness of each individual by calculating \( f(x) \) for each \( x \) value:

```{r}
# Evaluate fitness
fitness <- evaluate_fitness(population)
fitness
```

### Selection

We select individuals \( x_1 \) and \( x_2 \) as parents for crossover because they have higher fitness.

```{r}
# Perform selection
selected_parents <- selection(population, fitness, num_parents = 2)
selected_parents
```

### Crossover and Mutation

We perform crossover and mutation on the selected parents to generate offspring: \( x_1' = 1 \), \( x_2' = 3 \).

```{r}
# Perform crossover
offspring <- crossover(selected_parents, offspring_size = 2)
offspring

# Perform mutation
mutated_offspring <- mutation(offspring, mutation_rate = 0.1)
mutated_offspring
```

### Replacement

We replace individual \( x_3 \) with offspring \( x_1' \), maintaining the population size.

```{r}
# Replace individuals in the population
new_population <- replacement(population, mutated_offspring, num_to_replace = 1)
new_population
```

```{r}
# Termination
# Repeat the above steps(from Evaluation) for multiple generations or until a termination condition is met.
```

## Warp-Up

This vignette demonstrates the usage of the `genetic.algo.optimizeR` package to optimize the given function using a genetic algorithm. Users can follow similar steps to optimize other functions or customize the parameters as needed.
